import os
import gdown
import tensorflow as tf
from deepface.commons import package_utils, folder_utils
from deepface.models.FacialRecognition import FacialRecognition
from deepface.commons import logger as log

logger = log.get_singletonish_logger()

tf_version = package_utils.get_tf_major_version()
if tf_version == 1:
    from keras.models import Model
    from keras.layers import Conv2D, ZeroPadding2D, Input, concatenate
    from keras.layers import Dense, Activation, Lambda, Flatten, BatchNormalization
    from keras.layers import MaxPooling2D, AveragePooling2D
    from keras import backend as K
else:
    from tensorflow.keras.models import Model
    from tensorflow.keras.layers import Conv2D, ZeroPadding2D, Input, concatenate
    from tensorflow.keras.layers import Dense, Activation, Lambda, Flatten, BatchNormalization
    from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D
    from tensorflow.keras import backend as K

# pylint: disable=unnecessary-lambda

# ---------------------------------------

# pylint: disable=too-few-public-methods
class OpenFaceClient(FacialRecognition):
    """
    OpenFace model class
    """

    def __init__(self):
        self.model = load_model()
        self.model_name = "OpenFace"
        self.input_shape = (96, 96)
        self.output_shape = 128


def load_model(
    url="https://github.com/serengil/deepface_models/releases/download/v1.0/openface_weights.h5",
) -> Model:
    """
    Consturct OpenFace model, download its weights and load
    Returns:
        model (Model)
    """
    myInput = Input(shape=(96, 96, 3))

    x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
    x = Conv2D(64, (7, 7), strides=(2, 2), name="conv1")(x)
    x = BatchNormalization(axis=3, epsilon=0.00001, name="bn1")(x)
    x = Activation("relu")(x)
    x = ZeroPadding2D(padding=(1, 1))(x)
    x = MaxPooling2D(pool_size=3, strides=2)(x)
    x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_1")(x)
    x = Conv2D(64, (1, 1), name="conv2")(x)
    x = BatchNormalization(axis=3, epsilon=0.00001, name="bn2")(x)
    x = Activation("relu")(x)
    x = ZeroPadding2D(padding=(1, 1))(x)
    x = Conv2D(192, (3, 3), name="conv3")(x)
    x = BatchNormalization(axis=3, epsilon=0.00001, name="bn3")(x)
    x = Activation("relu")(x)
    x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_2")(x)  # x is equal added
    x = ZeroPadding2D(padding=(1, 1))(x)
    x = MaxPooling2D(pool_size=3, strides=2)(x)

    # Inception3a
    inception_3a_3x3 = Conv2D(96, (1, 1), name="inception_3a_3x3_conv1")(x)
    inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_3x3_bn1")(
        inception_3a_3x3
    )
    inception_3a_3x3 = Activation("relu")(inception_3a_3x3)
    inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)
    inception_3a_3x3 = Conv2D(128, (3, 3), name="inception_3a_3x3_conv2")(inception_3a_3x3)
    inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_3x3_bn2")(
        inception_3a_3x3
    )
    inception_3a_3x3 = Activation("relu")(inception_3a_3x3)

    inception_3a_5x5 = Conv2D(16, (1, 1), name="inception_3a_5x5_conv1")(x)
    inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_5x5_bn1")(
        inception_3a_5x5
    )
    inception_3a_5x5 = Activation("relu")(inception_3a_5x5)
    inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)
    inception_3a_5x5 = Conv2D(32, (5, 5), name="inception_3a_5x5_conv2")(inception_3a_5x5)
    inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_5x5_bn2")(
        inception_3a_5x5
    )
    inception_3a_5x5 = Activation("relu")(inception_3a_5x5)

    inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)
    inception_3a_pool = Conv2D(32, (1, 1), name="inception_3a_pool_conv")(inception_3a_pool)
    inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_pool_bn")(
        inception_3a_pool
    )
    inception_3a_pool = Activation("relu")(inception_3a_pool)
    inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool)

    inception_3a_1x1 = Conv2D(64, (1, 1), name="inception_3a_1x1_conv")(x)
    inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_1x1_bn")(
        inception_3a_1x1
    )
    inception_3a_1x1 = Activation("relu")(inception_3a_1x1)

    inception_3a = concatenate(
        [inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3
    )

    # Inception3b
    inception_3b_3x3 = Conv2D(96, (1, 1), name="inception_3b_3x3_conv1")(inception_3a)
    inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_3x3_bn1")(
        inception_3b_3x3
    )
    inception_3b_3x3 = Activation("relu")(inception_3b_3x3)
    inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)
    inception_3b_3x3 = Conv2D(128, (3, 3), name="inception_3b_3x3_conv2")(inception_3b_3x3)
    inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_3x3_bn2")(
        inception_3b_3x3
    )
    inception_3b_3x3 = Activation("relu")(inception_3b_3x3)

    inception_3b_5x5 = Conv2D(32, (1, 1), name="inception_3b_5x5_conv1")(inception_3a)
    inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_5x5_bn1")(
        inception_3b_5x5
    )
    inception_3b_5x5 = Activation("relu")(inception_3b_5x5)
    inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)
    inception_3b_5x5 = Conv2D(64, (5, 5), name="inception_3b_5x5_conv2")(inception_3b_5x5)
    inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_5x5_bn2")(
        inception_3b_5x5
    )
    inception_3b_5x5 = Activation("relu")(inception_3b_5x5)

    inception_3b_pool = Lambda(lambda x: x**2, name="power2_3b")(inception_3a)
    inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool)
    inception_3b_pool = Lambda(lambda x: x * 9, name="mult9_3b")(inception_3b_pool)
    inception_3b_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_3b")(inception_3b_pool)
    inception_3b_pool = Conv2D(64, (1, 1), name="inception_3b_pool_conv")(inception_3b_pool)
    inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_pool_bn")(
        inception_3b_pool
    )
    inception_3b_pool = Activation("relu")(inception_3b_pool)
    inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool)

    inception_3b_1x1 = Conv2D(64, (1, 1), name="inception_3b_1x1_conv")(inception_3a)
    inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_1x1_bn")(
        inception_3b_1x1
    )
    inception_3b_1x1 = Activation("relu")(inception_3b_1x1)

    inception_3b = concatenate(
        [inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3
    )

    # Inception3c
    inception_3c_3x3 = Conv2D(128, (1, 1), strides=(1, 1), name="inception_3c_3x3_conv1")(
        inception_3b
    )
    inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3c_3x3_bn1")(
        inception_3c_3x3
    )
    inception_3c_3x3 = Activation("relu")(inception_3c_3x3)
    inception_3c_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3c_3x3)
    inception_3c_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name="inception_3c_3x3_conv" + "2")(
        inception_3c_3x3
    )
    inception_3c_3x3 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_3c_3x3_bn" + "2"
    )(inception_3c_3x3)
    inception_3c_3x3 = Activation("relu")(inception_3c_3x3)

    inception_3c_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name="inception_3c_5x5_conv1")(
        inception_3b
    )
    inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3c_5x5_bn1")(
        inception_3c_5x5
    )
    inception_3c_5x5 = Activation("relu")(inception_3c_5x5)
    inception_3c_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3c_5x5)
    inception_3c_5x5 = Conv2D(64, (5, 5), strides=(2, 2), name="inception_3c_5x5_conv" + "2")(
        inception_3c_5x5
    )
    inception_3c_5x5 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_3c_5x5_bn" + "2"
    )(inception_3c_5x5)
    inception_3c_5x5 = Activation("relu")(inception_3c_5x5)

    inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b)
    inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool)

    inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3)

    # inception 4a
    inception_4a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_4a_3x3_conv" + "1")(
        inception_3c
    )
    inception_4a_3x3 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "1"
    )(inception_4a_3x3)
    inception_4a_3x3 = Activation("relu")(inception_4a_3x3)
    inception_4a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3)
    inception_4a_3x3 = Conv2D(192, (3, 3), strides=(1, 1), name="inception_4a_3x3_conv" + "2")(
        inception_4a_3x3
    )
    inception_4a_3x3 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "2"
    )(inception_4a_3x3)
    inception_4a_3x3 = Activation("relu")(inception_4a_3x3)

    inception_4a_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name="inception_4a_5x5_conv1")(
        inception_3c
    )
    inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_4a_5x5_bn1")(
        inception_4a_5x5
    )
    inception_4a_5x5 = Activation("relu")(inception_4a_5x5)
    inception_4a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4a_5x5)
    inception_4a_5x5 = Conv2D(64, (5, 5), strides=(1, 1), name="inception_4a_5x5_conv" + "2")(
        inception_4a_5x5
    )
    inception_4a_5x5 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_4a_5x5_bn" + "2"
    )(inception_4a_5x5)
    inception_4a_5x5 = Activation("relu")(inception_4a_5x5)

    inception_4a_pool = Lambda(lambda x: x**2, name="power2_4a")(inception_3c)
    inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool)
    inception_4a_pool = Lambda(lambda x: x * 9, name="mult9_4a")(inception_4a_pool)
    inception_4a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_4a")(inception_4a_pool)

    inception_4a_pool = Conv2D(128, (1, 1), strides=(1, 1), name="inception_4a_pool_conv" + "")(
        inception_4a_pool
    )
    inception_4a_pool = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_4a_pool_bn" + ""
    )(inception_4a_pool)
    inception_4a_pool = Activation("relu")(inception_4a_pool)
    inception_4a_pool = ZeroPadding2D(padding=(2, 2))(inception_4a_pool)

    inception_4a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_4a_1x1_conv" + "")(
        inception_3c
    )
    inception_4a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_4a_1x1_bn" + "")(
        inception_4a_1x1
    )
    inception_4a_1x1 = Activation("relu")(inception_4a_1x1)

    inception_4a = concatenate(
        [inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3
    )

    # inception4e
    inception_4e_3x3 = Conv2D(160, (1, 1), strides=(1, 1), name="inception_4e_3x3_conv" + "1")(
        inception_4a
    )
    inception_4e_3x3 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "1"
    )(inception_4e_3x3)
    inception_4e_3x3 = Activation("relu")(inception_4e_3x3)
    inception_4e_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3)
    inception_4e_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name="inception_4e_3x3_conv" + "2")(
        inception_4e_3x3
    )
    inception_4e_3x3 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "2"
    )(inception_4e_3x3)
    inception_4e_3x3 = Activation("relu")(inception_4e_3x3)

    inception_4e_5x5 = Conv2D(64, (1, 1), strides=(1, 1), name="inception_4e_5x5_conv" + "1")(
        inception_4a
    )
    inception_4e_5x5 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "1"
    )(inception_4e_5x5)
    inception_4e_5x5 = Activation("relu")(inception_4e_5x5)
    inception_4e_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5)
    inception_4e_5x5 = Conv2D(128, (5, 5), strides=(2, 2), name="inception_4e_5x5_conv" + "2")(
        inception_4e_5x5
    )
    inception_4e_5x5 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "2"
    )(inception_4e_5x5)
    inception_4e_5x5 = Activation("relu")(inception_4e_5x5)

    inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a)
    inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool)

    inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3)

    # inception5a
    inception_5a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5a_3x3_conv" + "1")(
        inception_4e
    )
    inception_5a_3x3 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "1"
    )(inception_5a_3x3)
    inception_5a_3x3 = Activation("relu")(inception_5a_3x3)
    inception_5a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3)
    inception_5a_3x3 = Conv2D(384, (3, 3), strides=(1, 1), name="inception_5a_3x3_conv" + "2")(
        inception_5a_3x3
    )
    inception_5a_3x3 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "2"
    )(inception_5a_3x3)
    inception_5a_3x3 = Activation("relu")(inception_5a_3x3)

    inception_5a_pool = Lambda(lambda x: x**2, name="power2_5a")(inception_4e)
    inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool)
    inception_5a_pool = Lambda(lambda x: x * 9, name="mult9_5a")(inception_5a_pool)
    inception_5a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_5a")(inception_5a_pool)

    inception_5a_pool = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5a_pool_conv" + "")(
        inception_5a_pool
    )
    inception_5a_pool = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_5a_pool_bn" + ""
    )(inception_5a_pool)
    inception_5a_pool = Activation("relu")(inception_5a_pool)
    inception_5a_pool = ZeroPadding2D(padding=(1, 1))(inception_5a_pool)

    inception_5a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_5a_1x1_conv" + "")(
        inception_4e
    )
    inception_5a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_5a_1x1_bn" + "")(
        inception_5a_1x1
    )
    inception_5a_1x1 = Activation("relu")(inception_5a_1x1)

    inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3)

    # inception_5b
    inception_5b_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5b_3x3_conv" + "1")(
        inception_5a
    )
    inception_5b_3x3 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "1"
    )(inception_5b_3x3)
    inception_5b_3x3 = Activation("relu")(inception_5b_3x3)
    inception_5b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5b_3x3)
    inception_5b_3x3 = Conv2D(384, (3, 3), strides=(1, 1), name="inception_5b_3x3_conv" + "2")(
        inception_5b_3x3
    )
    inception_5b_3x3 = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "2"
    )(inception_5b_3x3)
    inception_5b_3x3 = Activation("relu")(inception_5b_3x3)

    inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a)

    inception_5b_pool = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5b_pool_conv" + "")(
        inception_5b_pool
    )
    inception_5b_pool = BatchNormalization(
        axis=3, epsilon=0.00001, name="inception_5b_pool_bn" + ""
    )(inception_5b_pool)
    inception_5b_pool = Activation("relu")(inception_5b_pool)

    inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool)

    inception_5b_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_5b_1x1_conv" + "")(
        inception_5a
    )
    inception_5b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_5b_1x1_bn" + "")(
        inception_5b_1x1
    )
    inception_5b_1x1 = Activation("relu")(inception_5b_1x1)

    inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3)

    av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b)
    reshape_layer = Flatten()(av_pool)
    dense_layer = Dense(128, name="dense_layer")(reshape_layer)
    norm_layer = Lambda(lambda x: K.l2_normalize(x, axis=1), name="norm_layer")(dense_layer)

    # Final Model
    model = Model(inputs=[myInput], outputs=norm_layer)

    # -----------------------------------

    home = folder_utils.get_deepface_home()

    if os.path.isfile(home + "/.deepface/weights/openface_weights.h5") != True:
        logger.info("openface_weights.h5 will be downloaded...")

        output = home + "/.deepface/weights/openface_weights.h5"
        gdown.download(url, output, quiet=False)

    # -----------------------------------

    model.load_weights(home + "/.deepface/weights/openface_weights.h5")

    # -----------------------------------

    return model
